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//use tensor_rs::tensor::Tensor;
//use auto_diff::var::{Module, Var, bcewithlogitsloss};
//fn alexnet(x: Var) {
// def __init__(self, num_classes=1000):
// super(AlexNet, self).__init__()
// self.features = nn.Sequential(
// nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=2),
// nn.ReLU(inplace=True),
// nn.MaxPool2d(kernel_size=3, stride=2),
// nn.Conv2d(64, 192, kernel_size=5, padding=2),
// nn.ReLU(inplace=True),
// nn.MaxPool2d(kernel_size=3, stride=2),
// nn.Conv2d(192, 384, kernel_size=3, padding=1),
// nn.ReLU(inplace=True),
// nn.Conv2d(384, 256, kernel_size=3, padding=1),
// nn.ReLU(inplace=True),
// nn.Conv2d(256, 256, kernel_size=3, padding=1),
// nn.ReLU(inplace=True),
// nn.MaxPool2d(kernel_size=3, stride=2),
// )
// self.avgpool = nn.AdaptiveAvgPool2d((6, 6))
// self.classifier = nn.Sequential(
// nn.Dropout(),
// nn.Linear(256 * 6 * 6, 4096),
// nn.ReLU(inplace=True),
// nn.Dropout(),
// nn.Linear(4096, 4096),
// nn.ReLU(inplace=True),
// nn.Linear(4096, num_classes),
// )
//
// def forward(self, x):
// x = self.features(x)
// x = self.avgpool(x)
// x = torch.flatten(x, 1)
// x = self.classifier(x)
// return x
//}